Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.

Area of Science:

• Single-cell biology
• Computational biology
• Immunology

Background:

• Cell-cell communication (CCC) is vital for coordinated cellular functions but is often obscured by traditional methods that aggregate data by cell type.
• Existing tools for inferring CCC typically mask critical single-cell heterogeneity and lack integrative analytical capabilities for complex signaling pathways.

Purpose of the Study:

• To develop an advanced computational framework, CCC-RISE, for analyzing cell-cell communication at single-cell resolution across multiple conditions.
• To overcome limitations of aggregate data analysis by identifying disease-relevant signaling subpopulations invisible to conventional methods.

Main Methods:

• CCC-RISE extends the tensor-based Reduction and Insight in Single-cell Exploration (RISE) method.
• It deconvolves single-cell communication data into interpretable modules based on sender cells, receiver cells, ligands, and condition associations.
• The framework was applied to COVID-19 and lung transplant allograft dysfunction cohorts.

Main Results:

• CCC-RISE successfully identified disease-relevant communication programs in both COVID-19 and lung transplant cohorts.
• The method pinpointed specific cellular subpopulations driving these communication programs, often crossing traditional cell-type boundaries.
• Disease-associated signaling subpopulations were revealed that are undetectable by aggregate analysis methods.

Conclusions:

• CCC-RISE provides a robust pipeline for integrative analysis of cell-cell communication at single-cell resolution.
• This approach enables the discovery of novel, disease-specific signaling mechanisms and cellular players.
• The findings highlight the importance of single-cell resolution for understanding complex biological systems and disease states.